摘要
针对一类具有死区模型并且控制增益符号已知的不确定多输入多输出非线性时滞系统,基于滑模控制原理提出了一种稳定的自适应神经网络控制方案。该方案通过使用Lyapunov-Krasovskii泛函抵消了因未知时变时滞带来的系统不确定性。通过利用积分型李亚普诺夫函数,并且构造逼近连续函数,闭环系统证明是半全局一致终结有界。仿真结果表明了该方法的有效性。
A design scheme of adaptive neural controller is proposed for a class of uncertain MIMO nonlinear time-varying delay system with unknown nonlinear dead-zones and known function control gain. The design is based on the principle of sliding mode control. The unknown time-varying delay uncertainties are compensated by using appropriate Lyapunov-Krasovskii Functionals in the design. By utilizing the integral Lyapunov Function and constructing approximated continuous functions, the closed-loop control systems is proved to be semi-globally uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the approach.
出处
《电光与控制》
北大核心
2009年第8期9-14,39,共7页
Electronics Optics & Control
基金
国家自然科学基金资助项目(60774017)
江苏省教育厅自然科学基金资助项目(07KJB520133)
关键词
自适应控制
神经网络
滑模控制
时变时滞
adaptive control
neural networks
sliding mode control
time-varying delays
